GPU infrastructure
Pooled, partitioned, or dedicated GPU architecture based on validated requirements.
AI Platform
Private AI built around enterprise control.
Build, train, and serve AI on infrastructure designed around your data, governance, and operating requirements.
Enterprise teamsApplications
Private AI servicesGoverned access · deployment-specific
TrainServeRAGMLOpsData
GPU-ready OpenStack foundationCustomer-controlled environment
Capability direction
From GPU infrastructure to governed AI consumption.
The platform is designed to bring infrastructure, orchestration, data services, and AI operations into one customer-specific architecture.
Model training
Training and fine-tuning environments can be designed around selected workloads.
Private inference
Model-serving endpoints inside customer-controlled environments.
RAG and vector data
Ground AI experiences in governed enterprise data.
MLOps
Lifecycle patterns for pipelines, tracking, registry, and controlled deployment.
AI consumption
Self-service and gateway capabilities are deployment-dependent.
AI capability pages
Explore deployment-dependent AI capabilities for GPU infrastructure, training, inference, RAG, private endpoints, and service models.
GPU ComputePlan GPU-ready infrastructure for private AI workloads with deployment-dependent sizing, validation, and operating scope.Deployment-dependent capabilityInference and LLM ServingPlan private inference and LLM serving environments with deployment-dependent model, GPU, security, and operations scope.Deployment-dependent capabilityPrivate LLM EndpointsPlan private LLM endpoint architecture with access controls, model validation, infrastructure scope, and operational governance.Deployment-dependent capabilityRAG and Vector DataPlan private RAG and vector data architecture with governance, retrieval, storage, and deployment-specific controls.Deployment-dependent capabilityTraining and MLOpsPlan controlled AI training and MLOps infrastructure with private cloud, governance, storage, and operations alignment.Deployment-dependent capabilityAI-in-a-BoxExplore deployment-dependent private AI appliance direction for controlled enterprise and edge environments.Deployment-dependent / roadmap-sensitive capabilityAI-as-a-ServicePlan a deployment-dependent private AI-as-a-Service operating model with governance, scope, support, and commercial validation.Deployment-dependent commercial model
Why private AI
Keep control decisions close to the data and workloads.
Private AI can support organizations that need deliberate choices around model access, data handling, infrastructure ownership, and operating cost.
Data governance
Design data flows around approved enterprise policies.
Infrastructure ownership
Operate on customer-controlled or approved infrastructure.
Open ecosystem
Select open technologies after compatibility validation.
Cost visibility
Model capacity and operations against defined workloads.
FAQ
Frequently asked questions
What is a private AI platform?
An AI environment — GPU compute, training, inference, and data services — operated on infrastructure the enterprise governs, rather than consumed from a shared public service.
What capabilities does the Intrisus AI Platform cover?
GPU infrastructure, model training and fine-tuning environments, private inference serving, RAG and vector data, private LLM endpoints, and MLOps lifecycle patterns. Availability of specific capabilities is deployment-dependent.
Does enterprise data leave our environment?
The platform is designed so AI workloads and data flows run inside customer-controlled boundaries with governed access. Exact data paths are defined and validated per deployment.
How do we get started with private AI?
Through an AI workshop: assess workloads and data boundaries, validate GPU and infrastructure requirements, then design a deployment scope before commitment.
Next step
Define a private AI architecture around your data and use cases.
Start with your workloads, operating model, and control requirements.